• Advances in Energy Research
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• v.6 no.2
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• Journal of The Korean Society of Agricultural Engineers
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• v.55 no.4
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• pp.73-82
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• 2013

The main objective of this study was to evaluate Representative Concentration Pathways (RCP) scenarios-based flood risk at a Si-Gun level. A bias correction using a quantile mapping method with the Generalized Extreme Value (GEV) distribution was performed to correct future precipitation data provided by the Korea Meteorological Administration (KMA). A series of proxy variables including CN80 (Number of days over 80 mm) and CX3h (Maximum precipitation during 3-hr) etc. were used to carry out flood risk assessment. Indicators were normalized by a Z-score method and weighted by factors estimated by principal component analysis (PCA). Flood risk evaluation was conducted for the four different time periods, i.e. 1990s, 2025s, 2055s, and 2085s, which correspond to 1976~2005, 2011~2040, 2041~2070, and 2071~2100. The average flood risk indices based on RCP4.5 scenario were 0.08, 0.16, 0.22, and 0.13 for the corresponding periods in the order of time, which increased steadily up to 2055s period and decreased. The average indices based on RCP8.5 scenario were 0.08, 0.23, 0.11, and 0.21, which decreased in the 2055s period and then increased again. Considering the average index during entire period of the future, RCP8.5 scenario resulted in greater risk than RCP4.5 scenario.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.28-34
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• 2021

The Fourth Industrial Revolution has led to the development of drones for commercial and private applications. Therefore, the malfunction of drones has become a prominent problem. Failure mode and effect analysis was used in this study to analyze the primary cause of drone failure, and blade breakage was observed to have the highest frequency of failure. This was tested using a vibration sensor placed on drones along the breakage length of the blades. The data exhibited a significant increase in vibration within the drone body for blade fracture length. Principal component analysis was used to reduce the data dimension and classify the state with machine learning algorithms such as support vector machine, k-nearest neighbor, Gaussian naive Bayes, and random forest. The performance of machine learning was higher than 0.95 for the four algorithms in terms of accuracy, precision, recall, and f1-score. A follow-up study on failure prediction will be conducted based on the results of fault diagnosis.

• The Korean Journal of Food And Nutrition
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• v.34 no.1
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• pp.1-14
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• 2021

This study evaluated the antioxidant activity and quality characteristics of rice cookie with added butterbur powder in a ratio of 0, 5, 10, 15, and 20% to confirm the possibility of butterbur as a functional food. The moisture content, spread factor, leavening rate, and hardness of rice cookies increased with an increase in the amount of butterbur powder; whereas a decrease in the pH and baking loss rate was observed. The L and b values decreased as the amount of butterbur powder increased, but the value was the lowest when 5% of butterbur powder was added. The sensory liking score showed the highest preference for 10% butterbur powder regarding appearance, flavor, taste, texture, and overall preference. In the principal component analysis (PCA), the addition of 10% butterbur powder positively affected the measure of food acceptance in terms of organoleptic properties of butterbur. Besides, as the amount of added butterbur powder increased, the antioxidant activity of rice cookies increased. Based on these results, it appears that the addition of butterbur powder to rice cookies in a 10% ratio can produce rice cookies with excellent antioxidant activity, overall quality, and high preference.

• Journal of the Korea Convergence Society
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• v.7 no.4
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• pp.1-7
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• 2016

Structural MRI(sMRI) imaging is used to extract morphometric features after Grey Matter (GM), White Matter (WM) for several univariate and multivariate method, and Cerebro-spinal Fluid (CSF) segmentation. A new approach is applied for the diagnosis of very mild to mild AD. We propose the classification method of Alzheimer disease patients from normal controls by combining morphometric features and Gaussian Mixture Models parameters along with MMSE (Mini Mental State Examination) score. The combined features are fed into Multi-kernel SVM classifier after getting rid of curse of dimensionality using principal component analysis. The experimenral results of the proposed diagnosis method yield up to 96% stratification accuracy with Multi-kernel SVM along with high sensitivity and specificity above 90%.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.3
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• pp.297-302
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• 2008

Application of genotype by environment ($G\;{\times}\;E$) interaction would be used for identifying optimum test condition of the varietal adaptation in the establishment of breeding purpose. Yield and yield components were used to perform additive main effect and multiplicative interaction (AMMI) analysis. Significant difference for $G\;{\times}\;E$ interaction were observed for all variable examined. For yield, 0.18 of total sum of squares corresponded to $G\;{\times}\;E$ interaction. Correlation analysis was carried out between genotypic scores of the first interaction principal component axis (IPCA 1) for agronomic characters. Significant correlations were observed between IPCA 1 for yield and capsule bearing stem length (CBSL), number of capsule per plant (NOC). The biplot of grain yield means for IPCA1 which accounted for 34% of the variation in total treatment sums of squares showed different reaction according to $G\;{\times}\;E$ interaction, genotypes and environments. Taegu showed relatively lower positive IPCA1 scores, and it also showed smaller coefficient variation of yield mean where it is recommendable as a optimal site for the sesame cultivar adaptation and evaluation trial. In case of variables, Yangbaek and M1 showed relatively lower IPCA1 scores, but the score direction showed opposite each other on the graph. Ansan, Miryang1, Miryang4, and Miryang6 seemed to be similar group in view of yield response against IPCA1 scores. These results will be helpful to select experimental site for sesame in Korea to minimize $G\;{\times}\;E$ interaction for the selection of promising genotype with higher stability.

• Journal of the Korean Wood Science and Technology
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• v.46 no.3
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• pp.285-296
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• 2018

As a study for the verification of heat treated wood according to ISPM No. 15, the spectroscopic characteristics of the heat treated wood surface were analyzed. Various functional groups were observed on the IR spectrum, but it was difficult to find any particular difference between wood species, heat treatment time and storage period. HBI (hydrogen-bonding intensity) shows the change of the heat treated wood according to the storage time, but the change of wood with the heat treatment time was hard to be observed. On the PCA score plot, however, it was possible to sort the wood according to the heat treatment time of 60 minutes or 90 minutes in the species. The standards for classification of heat-treated wood in PCA were aromatic rings in lignin and C-H bending in cellulose, and these components were able to classify heat-treated wood by ISPM No. 15.

• Anxiety and mood
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• v.17 no.2
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• pp.73-77
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• 2021

Objective : The Hoarding Rating Scale-Self-Report (HRS-SR) is a five-item scale that simply assesses the hoarding symptoms. We evaluated the factor structure, reliability, and validity of the Korean version of the HRS-SR (HRS-SR-K). Methods : A total of 144 individuals completed the self-administered questionnaires including HRS-SR-K, Obsessive-Compulsive Inventory-Revised-Korean version (OCI-R-K), Beck Depression Inventory (BDI), and Beck Anxiety Inventory (BAI). Psychometric properties of the HRS-SR-K were analyzed. Results : The Cronbach's α value for internal consistency of the HRS-SR-K was excellent (Cronbach's α=0.84). The construct validity was analyzed on the basis of principal component analysis and one-factor structure of the original scale was maintained. The HRS-SR-K total score and each item scores were more strongly correlated with the hoarding subscale score in OCI-R-K (convergent validity, r=0.71, p<0.01) than the corresponding scores of nonspecific depression or anxiety measures (discriminant validity). Conclusion : The HRS-SR-K is a simple and reliable self-report scale for examining the severity of hoarding symptoms.

• Journal of Korean Society of Forest Science
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• v.77 no.1
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• pp.73-82
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• 1988

This study was excuted to analyze the vegetational community structure of the Hongrudong valley the Mt. Gaya by three kinds of ordination techniques (polar, principal component analysis, reciprocal averaging). Eleven sites were sampled with the clumped method to analyze the vegetation structure. The result suggested that Hongrudong valley forest was divided by Pinus densiflora and Quercus aliena community. The relation between stand scores of ordination and soil pH, humus content, soil moisture had a tendency to increase significantly from P. densiflora to Q. aliena community. RA was the most effective method of this study. RA ordination was showed that successional trends of tree species seem to be from P. densijlora through Q. variabilis to Q. aliena, Carpinus laxiflora in the upper layer and from Lespedeza cyrtobotrva, Rhus spp., Rhododendron schlippenbachii through Fraxinus sieboldiana, Lindera obtusiloba to Euonymus oxyphyllus, Weigela subsessilis, Callicarpa japonica in the middle layer.

• Journal of Korean Society of Forest Science
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• v.82 no.2
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• pp.176-187
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• 1993

Based on the analysis of ecomorphological characters for 84 tree and shrub species, climax indices were estimated so as to interpret the position of the successional sere for the species in the natural deciduous forest. Nineteen ecomorphological characters, considered to be associated with successional gradient in the forest, were selected for the study. One of 2 to 4 steps per character for each species was given on a standardized scale of increasing climaxness, and the index was computed by percent of the sum of the scoring values for total score. Calculated mean value of 54.2 for all indices. Carpinus laxiflora had the highest index value of 83.3, and Populus davidiana recorded the lowest of 18.8. The most climax group, greater than 70 of the index, contained only 9 species, intermediate group, between 40 to 70 of the index, had 58 species, and the most pioneer group, less than 40 of the index comprised 17 species. The result has noticed that the large number of species would take advantage of most diverse resource and niche in the intermediate stage of the sere in the forest. The three components, i.e., light absorption, reproduction, and wood quality were used as axes for a 3-dimensional projection of the relative position for 44 species by principal component analysis. Along the similar ecomorphological characters, four recognized species group were classified by cluster analysis. The distribution pattern of plant families on the index gradient showed that the Betulaceae and Aceraceae had the widest seral amplitude, and the Salicaceae was a family typified as pioneer. There were no families specializing entirely with climax niche.